Examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from oclock import Timer, loop, interactiveloop\n",
    "import time, random, threading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constant-duration loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the `Timer.checkpt()` method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code executes a print statement every 2 seconds and stops when the elapsed time of the loop exceeds 10 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 0.000\n",
      "Elapsed time: 2.000\n",
      "Elapsed time: 4.000\n",
      "Elapsed time: 6.001\n",
      "Elapsed time: 8.002\n",
      "Elapsed time: 10.002\n"
     ]
    }
   ],
   "source": [
    "timer = Timer(interval=2)\n",
    "while timer.elapsed_time < 11:\n",
    "    print(\"Elapsed time: {:.3f}\".format(timer.elapsed_time))\n",
    "    timer.checkpt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the `@loop` decorator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code does the same thing as the code above. In this simple situation, using the decorator is slightly more verbose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elapsed time: 2.000\n",
      "Elapsed time: 4.001\n",
      "Elapsed time: 6.001\n",
      "Elapsed time: 8.001\n",
      "Elapsed time: 10.002\n"
     ]
    }
   ],
   "source": [
    "timer = Timer(interval=2)\n",
    "@loop(timer)\n",
    "def my_function():\n",
    "    \"\"\"Example function to be repeated periodically\"\"\"\n",
    "    print(\"Elapsed time: {:.3f}\".format(timer.elapsed_time))\n",
    "    if timer.elapsed_time > 10:\n",
    "        timer.stop()\n",
    "my_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactive, cancellable loops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the `@interactiveloop` decorator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the timer is internal to the loop and cannot be accessed programatically, but a command line interface allows the user to interact dynamically with the Timer (e.g. change time interval, pause, reset etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------------------\n",
      "Timer Command-Line-Interface. Possible inputs:\n",
      "- any number (int/float): change timer interval to that new value\n",
      "- 'p' or 'pause': pause timer\n",
      "- 'r' or 'resume': resume timer\n",
      "- 'R' or 'reset': reset timer\n",
      "- 't' or 'time': print timing (interval, elapsed time, etc.) info\n",
      "- 'q', 'Q', 'quit' or 'stop': stop timer and exit\n",
      "-----------------------------------------------------------------\n",
      "This is a random number: 0.598\n",
      "\n",
      "This is a random number: 0.457\n",
      "This is a random number: 0.549\n",
      "This is a random number: 0.655\n",
      "--- Timer Paused\n",
      "This is a random number: 0.596\n",
      "--- Timer Resumed\n",
      "This is a random number: 0.737\n",
      "This is a random number: 0.868\n",
      "This is a random number: 0.116\n",
      "--- Timer Stopped\n",
      "--- Loop Exited\n"
     ]
    }
   ],
   "source": [
    "@interactiveloop(interval=2)\n",
    "def interactive_function():\n",
    "    \"\"\"Example function to be repeated periodically\"\"\"\n",
    "    print(\"This is a random number: {:.3f}\".format(random.random()))\n",
    "interactive_function()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using `Timer` methods and threading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To give an idea how to implement an interactive timer similar to the `@interactiveloop` decorator, here is a minimal example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Define the command line interface (CLI) to interact with the timer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_input(timer):\n",
    "    \"\"\"Command line input to interact with a timer object.\"\"\"\n",
    "    print('Input command or press enter to exit\\n')\n",
    "    while not timer.is_stopped:\n",
    "        a = input()\n",
    "        try:\n",
    "            dt = float(a)\n",
    "        except ValueError:\n",
    "            if a == 'pause':\n",
    "                timer.pause()\n",
    "            elif a == 'resume':\n",
    "                timer.resume()\n",
    "            elif a == 'reset':\n",
    "                timer.reset()\n",
    "            else:\n",
    "                timer.stop()\n",
    "        else:\n",
    "            timer.interval = dt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Run the CLI and the timer concurrently using a thread:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input command or press enter to exit\n",
      "This is a random number: 0.119\n",
      "\n",
      "This is a random number: 0.862\n",
      "This is a random number: 0.545\n",
      "This is a random number: 0.995\n",
      "This is a random number: 0.517\n",
      "This is a random number: 0.055\n"
     ]
    }
   ],
   "source": [
    "timer = Timer()\n",
    "threading.Thread(target=user_input, args=(timer,)).start()\n",
    "while not timer.is_stopped:\n",
    "    print(\"This is a random number: {:.3f}\".format(random.random()))\n",
    "    timer.checkpt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change timer interval in real time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_interval_change(immediate=True, precise=False):\n",
    "    \"\"\"Note: takes approximately 10 seconds to run\"\"\"\n",
    "    \n",
    "    print(f'Test started (immediate {immediate}, precise {precise})')\n",
    "\n",
    "    timer1 = Timer(interval=0.1, precise=precise)\n",
    "    timer2 = Timer(interval=0.07, precise=precise)\n",
    "    tmax = 10\n",
    "\n",
    "    def change_interval():\n",
    "        targets = 2, 4, 7, 10, 15\n",
    "        i = 0\n",
    "        while timer2.elapsed_time <= tmax:\n",
    "            if timer2.elapsed_time >= targets[i]:\n",
    "                new_interval = timer1.interval + 0.1\n",
    "                timer1.set_interval(new_interval, immediate=immediate)\n",
    "                i += 1\n",
    "                print('Interval change number', i, 'to dt =', timer1.interval)\n",
    "            timer2.checkpt()\n",
    "\n",
    "\n",
    "    def get_times():\n",
    "        times = []\n",
    "        timer1.reset()\n",
    "        while timer1.elapsed_time <= tmax:\n",
    "            times.append(time.perf_counter())\n",
    "            timer1.checkpt()\n",
    "        return times\n",
    "\n",
    "\n",
    "    threading.Thread(target=change_interval).start()\n",
    "    times = get_times()\n",
    "    \n",
    "    dt = np.diff(times)\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.plot(dt, '-ok')\n",
    "    ax.set_xlabel('timer iteration')\n",
    "    ax.set_ylabel(r'$\\Delta t$ (s)')\n",
    "    ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Immediately cancellable timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test started (immediate True, precise False)\n",
      "Interval change number 1 to dt = 0.2\n",
      "Interval change number 2 to dt = 0.30000000000000004\n",
      "Interval change number 3 to dt = 0.4\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_interval_change(immediate=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note in the graph above the smaller intervals every time the timer interval is changed. This is because with `oclock.Timer` the time interval changes are effective immediately by default. If this is a problem, it is possible to use the `immediate=True` option in `Timer.set_interval()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test started (immediate False, precise False)\n",
      "Interval change number 1 to dt = 0.2\n",
      "Interval change number 2 to dt = 0.30000000000000004\n",
      "Interval change number 3 to dt = 0.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_interval_change(immediate=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, note that loops can be more precise when using the `precise=True` option of the timer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test started (immediate False, precise True)\n",
      "Interval change number 1 to dt = 0.2\n",
      "Interval change number 2 to dt = 0.30000000000000004\n",
      "Interval change number 3 to dt = 0.4\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_interval_change(immediate=False, precise=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}